TP2_LIME
2022-12-05
Setup
# install.packages("keras")
# install.packages("lime")
# system('add-apt-repository -y ppa:cran/imagemagick')
# system('apt-get update')
# system("apt-get install libmagick++-dev")
# install.packages("magick")
# install.packages('abind')library(keras)
library(lime)
library(magick) ## Linking to ImageMagick 6.9.10.23
## Enabled features: fontconfig, freetype, fftw, lcms, pango, webp, x11
## Disabled features: cairo, ghostscript, heic, raw, rsvg## Using 4 threadslibrary(ggplot2)
library(abind)
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:lime':
##
## explain## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionutils
to_df <- function(explanation){
explanation_df <- data.frame(model_type = explanation$model_type,
case = explanation$case,
label = explanation$label,
label_prob = explanation$label_prob,
model_r2 = explanation$model_r2,
model_intercept = explanation$model_intercept,
model_prediction = explanation$model_prediction,
feature = explanation$feature,
feature_value = NA,
feature_weight = explanation$feature_weight,
feature_desc = explanation$feature_desc,
data = NA,
prediction = NA,
stringsAsFactors = FALSE)
## this will coerce superpixel_list and bitmap_list to list, but still works for plotting
for (i in 1:nrow(explanation_df)){
explanation_df$feature_value[i] <- explanation$feature_value[i]
explanation_df$data[i] <- explanation$data[i]
explanation_df$prediction[i] <- explanation$prediction[i]
}
return(explanation_df)
}
image_prep <- function(x) {
arrays <- lapply(x, function(path) {
img <- image_load(path, target_size = c(224,224))
x <- image_to_array(img)
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)
})
do.call(abind::abind, c(arrays, list(along = 1)))
}Modelos
model_vgg16 <- application_vgg16(weights = "imagenet", include_top = TRUE)## Loaded Tensorflow version 2.10.0model_resnet50 <- application_resnet50(weights = "imagenet", include_top = TRUE)
model_labels <- readRDS(system.file('extdata', 'imagenet_labels.rds', package = 'lime'))Explainers
explainer_vgg16 <- lime(c('flower_0269.jpg'), as_classifier(model_vgg16, model_labels), image_prep)
explainer_resnet50 <- lime(c('flower_0269.jpg'), as_classifier(model_resnet50, model_labels), image_prep)object explanation
expl_flower_0269 <- lime::explain(c('flower_0269.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 100,
background = "white"
)#plot_image_explanation(expl_flower_0269)#expl_flower_0269[1,]class(expl_flower_0269)## [1] "tbl_df" "tbl" "data.frame"expl_template <- .load_image_example()
class(expl_template)## [1] "data.frame"expl_template## model_type case label label_prob model_r2
## 1 classification produce.png strawberry 0.3546817 0.4310426
## 2 classification produce.png strawberry 0.3546817 0.4310426
## 3 classification produce.png strawberry 0.3546817 0.4310426
## 4 classification produce.png strawberry 0.3546817 0.4310426
## 5 classification produce.png strawberry 0.3546817 0.4310426
## 6 classification produce.png strawberry 0.3546817 0.4310426
## 7 classification produce.png strawberry 0.3546817 0.4310426
## 8 classification produce.png strawberry 0.3546817 0.4310426
## 9 classification produce.png strawberry 0.3546817 0.4310426
## 10 classification produce.png strawberry 0.3546817 0.4310426
## 11 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 12 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 13 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 14 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 15 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 16 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 17 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 18 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 19 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 20 classification produce.png candle, taper, wax light 0.1550600 0.3906374
## 21 classification produce.png Granny Smith 0.1225764 0.1457302
## 22 classification produce.png Granny Smith 0.1225764 0.1457302
## 23 classification produce.png Granny Smith 0.1225764 0.1457302
## 24 classification produce.png Granny Smith 0.1225764 0.1457302
## 25 classification produce.png Granny Smith 0.1225764 0.1457302
## 26 classification produce.png Granny Smith 0.1225764 0.1457302
## 27 classification produce.png Granny Smith 0.1225764 0.1457302
## 28 classification produce.png Granny Smith 0.1225764 0.1457302
## 29 classification produce.png Granny Smith 0.1225764 0.1457302
## 30 classification produce.png Granny Smith 0.1225764 0.1457302
## model_intercept model_prediction feature feature_value feature_weight
## 1 0.063138829 0.365205724 50 2994px superpixel 0.1394982102
## 2 0.063138829 0.365205724 42 1492px superpixel 0.1014987675
## 3 0.063138829 0.365205724 56 3097px superpixel 0.0816106280
## 4 0.063138829 0.365205724 43 2563px superpixel 0.0768424380
## 5 0.063138829 0.365205724 38 1815px superpixel -0.0533579953
## 6 0.063138829 0.365205724 54 3166px superpixel 0.0441655821
## 7 0.063138829 0.365205724 41 2254px superpixel -0.0473633081
## 8 0.063138829 0.365205724 26 1252px superpixel -0.0436434659
## 9 0.063138829 0.365205724 44 928px superpixel 0.0402079540
## 10 0.063138829 0.365205724 20 2744px superpixel -0.0373919153
## 11 0.202270823 0.510950719 20 2744px superpixel 0.1572028999
## 12 0.202270823 0.510950719 38 1815px superpixel 0.1361720436
## 13 0.202270823 0.510950719 9 3111px superpixel -0.1143510264
## 14 0.202270823 0.510950719 41 2254px superpixel 0.1189565730
## 15 0.202270823 0.510950719 36 1156px superpixel 0.1213101689
## 16 0.202270823 0.510950719 8 521px superpixel -0.1072294739
## 17 0.202270823 0.510950719 18 2093px superpixel 0.0869311809
## 18 0.202270823 0.510950719 39 3041px superpixel 0.0607053823
## 19 0.202270823 0.510950719 2 1538px superpixel -0.0774141241
## 20 0.202270823 0.510950719 42 1492px superpixel -0.0736037281
## 21 -0.002948256 0.007209071 52 2633px superpixel 0.0017020169
## 22 -0.002948256 0.007209071 51 3010px superpixel 0.0014061881
## 23 -0.002948256 0.007209071 9 3111px superpixel 0.0014092894
## 24 -0.002948256 0.007209071 59 926px superpixel 0.0013329257
## 25 -0.002948256 0.007209071 43 2563px superpixel -0.0013316915
## 26 -0.002948256 0.007209071 45 1040px superpixel 0.0011713706
## 27 -0.002948256 0.007209071 21 1220px superpixel 0.0013799368
## 28 -0.002948256 0.007209071 16 1893px superpixel 0.0010632344
## 29 -0.002948256 0.007209071 39 3041px superpixel 0.0010874609
## 30 -0.002948256 0.007209071 8 521px superpixel 0.0009365956
## feature_desc data
## 1 [206-269], [250-323] 4 channel 323x311 bitmap
## 2 [167-221], [260-319] 4 channel 323x311 bitmap
## 3 [252-311], [131-202] 4 channel 323x311 bitmap
## 4 [172-248], [232-278] 4 channel 323x311 bitmap
## 5 [163-218], [106-174] 4 channel 323x311 bitmap
## 6 [237-311], [171-242] 4 channel 323x311 bitmap
## 7 [165-224], [67-134] 4 channel 323x311 bitmap
## 8 [108-146], [57-120] 4 channel 323x311 bitmap
## 9 [185-245], [200-247] 4 channel 323x311 bitmap
## 10 [64-131], [1-62] 4 channel 323x311 bitmap
## 11 [64-131], [1-62] 4 channel 323x311 bitmap
## 12 [163-218], [106-174] 4 channel 323x311 bitmap
## 13 [19-70], [49-160] 4 channel 323x311 bitmap
## 14 [165-224], [67-134] 4 channel 323x311 bitmap
## 15 [146-179], [97-163] 4 channel 323x311 bitmap
## 16 [15-52], [172-200] 4 channel 323x311 bitmap
## 17 [52-128], [35-97] 4 channel 323x311 bitmap
## 18 [164-219], [17-86] 4 channel 323x311 bitmap
## 19 [1-44], [37-78] 4 channel 323x311 bitmap
## 20 [167-221], [260-319] 4 channel 323x311 bitmap
## 21 [216-279], [27-95] 4 channel 323x311 bitmap
## 22 [209-283], [77-143] 4 channel 323x311 bitmap
## 23 [19-70], [49-160] 4 channel 323x311 bitmap
## 24 [272-292], [43-114] 4 channel 323x311 bitmap
## 25 [172-248], [232-278] 4 channel 323x311 bitmap
## 26 [186-262], [11-43] 4 channel 323x311 bitmap
## 27 [64-136], [119-175] 4 channel 323x311 bitmap
## 28 [49-103], [135-192] 4 channel 323x311 bitmap
## 29 [164-219], [17-86] 4 channel 323x311 bitmap
## 30 [15-52], [172-200] 4 channel 323x311 bitmapcolnames(expl_flower_0269)## [1] "model_type" "case" "label" "label_prob"
## [5] "model_r2" "model_intercept" "model_prediction" "feature"
## [9] "feature_value" "feature_weight" "feature_desc" "data"
## [13] "prediction"explanations <- lime::explain(c('flower_0269.jpg','motorbike_0032.jpg'), explainer_vgg16,
n_labels = 5,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
to_df(explanations) %>%
ggplot(aes(x = feature_weight)) +
facet_wrap(~ case + label, scales = "free") +
geom_density()
Resultados
Moto
#Imagen original a clasificar
print(image_read('motorbike_0032.jpg'))## # A tibble: 1 × 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 JPEG 200 136 sRGB FALSE 20663 72x72
#Visualizamos los superpixels obtenidos de la imagen
plot_superpixels('motorbike_0032.jpg', n_superpixels = 50, weight = 5)
VGG16
#Clasificacion de la imagen segun el modelo VGG16
res_vgg16_motorbike <- predict(model_vgg16, image_prep(c('motorbike_0032.jpg')))
imagenet_decode_predictions(res_vgg16_motorbike)## [[1]]
## class_name class_description score
## 1 n03127747 crash_helmet 0.47350988
## 2 n03785016 moped 0.27609763
## 3 n03208938 disk_brake 0.11000329
## 4 n03792782 mountain_bike 0.09415597
## 5 n03791053 motor_scooter 0.02573411#Explain
explanation <- lime::explain(c('motorbike_0032.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 100,
n_superpixels = 100, weight = 10,
n_permutations = 500,
background = "white")plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = TRUE,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
plot_features(explanation, ncol = 3, cases = NULL)
explanation %>%
ggplot(aes(x = feature_weight)) +
facet_wrap(~ case, scales = "free") +
geom_density()
plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = FALSE,
display = "block", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
RESNET50
explanation <- lime::explain(c('motorbike_0032.jpg'), explainer_resnet50,
n_labels = 1,
n_features = 50,
n_superpixels = 50, weight = 10,
n_permutations = 500,
background = "white")plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = TRUE,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
plot_features(explanation, ncol = 3, cases = NULL)
plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = FALSE,
display = "block", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
Flor
#Imagen original a clasificar
plot(image_read('flower_0269.jpg'))
#Visualizamos los superpixels obtenidos de la imagen
plot_superpixels('flower_0269.jpg', n_superpixels = 50, weight = 10)
VGG16
res_vgg16_flower <- predict(model_vgg16, image_prep(c('flower_0269.jpg')))
imagenet_decode_predictions(res_vgg16_flower)## [[1]]
## class_name class_description score
## 1 n03991062 pot 0.22525069
## 2 n07720875 bell_pepper 0.09684577
## 3 n02206856 bee 0.06948115
## 4 n03457902 greenhouse 0.05719404
## 5 n03930313 picket_fence 0.04914082explanation <- lime::explain(c('flower_0269.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 50,
n_superpixels = 50, weight = 10,
n_permutations = 500,
background = "white")plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = TRUE,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
plot_features(explanation, ncol = 3, cases = NULL)
explanation %>%
ggplot(aes(x = feature_weight)) +
facet_wrap(~ case, scales = "free") +
geom_density()
plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = FALSE,
display = "block", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
RESNET50
res_resnet50_flower <- predict(model_resnet50, image_prep(c('flower_0269.jpg')))
imagenet_decode_predictions(res_resnet50_flower)## [[1]]
## class_name class_description score
## 1 n03991062 pot 0.40786061
## 2 n03457902 greenhouse 0.15969767
## 3 n07714571 head_cabbage 0.07762894
## 4 n07717410 acorn_squash 0.06562954
## 5 n07718472 cucumber 0.05744600explanation <- lime::explain(c('flower_0269.jpg'), explainer_resnet50,
n_labels = 1,
n_features = 50,
n_superpixels = 50, weight = 10,
n_permutations = 500,
background = "white")plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = TRUE,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = FALSE,
display = "block", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
Perro
#Imagen original a clasificar
plot(image_read('dog_0170.jpg'))
#Visualizamos los superpixels obtenidos de la imagen
plot_superpixels('dog_0170.jpg', n_superpixels = 50, weight = 10)
#### VGG16
#Clasificacion de la imagen segun el modelo VGG16
res_vgg16_dog <- predict(model_vgg16, image_prep(c('dog_0170.jpg')))
imagenet_decode_predictions(res_vgg16_dog)## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.630665898
## 2 n02093256 Staffordshire_bullterrier 0.280850619
## 3 n02110806 basenji 0.064215504
## 4 n02108915 French_bulldog 0.006692672
## 5 n02108089 boxer 0.005174202explanation <- lime::explain(c('dog_0170.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 50,
n_superpixels = 50, weight = 10,
n_permutations = 500,
background = "white")plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = TRUE,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
RESNET50
#Clasificacion de la imagen segun el modelo VGG16
res_resnet50_dog <- predict(model_resnet50, image_prep(c('dog_0170.jpg')))
imagenet_decode_predictions(res_resnet50_dog)## [[1]]
## class_name class_description score
## 1 n02093256 Staffordshire_bullterrier 0.572505355
## 2 n02093428 American_Staffordshire_terrier 0.231044978
## 3 n02091032 Italian_greyhound 0.142449960
## 4 n02110806 basenji 0.042962722
## 5 n02091134 whippet 0.005982167explanation <- lime::explain(c('dog_0170.jpg'), explainer_resnet50,
n_labels = 1,
n_features = 50,
n_superpixels = 50, weight = 10,
n_permutations = 500,
background = "white")plot_image_explanation(
to_df(explanation),
which = 1,
threshold = 0.02,
show_negative = TRUE,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red"),
block_col = "grey"
)
Evaluación
expl_flower_0269_vgg16 <- lime::explain(c('flower_0269.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
expl_motorbike_0032_vgg16 <- lime::explain(c('motorbike_0032.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
expl_dog_0170_vgg16 <- lime::explain(c('dog_0170.jpg'), explainer_vgg16,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
expl_flower_0269_resnet50 <- lime::explain(c('flower_0269.jpg'), explainer_resnet50,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
expl_motorbike_0032_resnet50 <- lime::explain(c('motorbike_0032.jpg'), explainer_resnet50,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
expl_dog_0170_resnet50 <- lime::explain(c('dog_0170.jpg'), explainer_resnet50,
n_labels = 1,
n_features = 10,
n_superpixels = 50, weight = 1,
n_permutations = 200,
background = "white"
)
explanations = c(expl_flower_0269_vgg16, expl_flower_0269_resnet50, expl_motorbike_0032_vgg16, expl_motorbike_0032_resnet50, expl_dog_0170_vgg16, expl_dog_0170_resnet50)
explanations_vgg16 <- c(expl_flower_0269_vgg16, expl_motorbike_0032_vgg16, expl_dog_0170_vgg16)
explanations_resnet50 <- c(expl_flower_0269_resnet50, expl_motorbike_0032_resnet50, expl_dog_0170_resnet50)for (explanation in explanations){
p <- to_df(explanations) %>%
ggplot(aes(x = feature_weight)) +
facet_wrap(~ case + label, scales = "free") +
geom_density()
print(p)
}
plot_image_explanation(
to_df(expl_motorbike_0032_vgg16),
which = 1,
threshold = 0.0,
display = "outline", #"outline"
fill_alpha = 0.3,
outline_col = c("blue", "red")
)
plot_image_explanation(to_df(expl_motorbike_0032_vgg16), display='block', block_col = "white", threshold=0.02)
plot_image_explanation(to_df(expl_motorbike_0032_vgg16), display='block', block_col = "white", threshold=0.015)
plot_image_explanation(to_df(expl_motorbike_0032_vgg16), display='block', block_col = "white", threshold=0.01)
plot_image_explanation(to_df(expl_flower_0269_vgg16), display='block', block_col = "white", threshold=0.03)
plot_image_explanation(to_df(expl_flower_0269_vgg16), display='block', block_col = "white", threshold=0.02)
plot_image_explanation(to_df(expl_flower_0269_vgg16), display='block', block_col = "white", threshold=0.015)
plot_image_explanation(to_df(expl_flower_0269_vgg16), display='block', block_col = "white", threshold=0.005)
plot_features(to_df(expl_dog_0170_vgg16))
plot_image_explanation(to_df(expl_dog_0170_vgg16), display='block', block_col = "white", threshold=0.03)
plot_image_explanation(to_df(expl_dog_0170_vgg16), display='block', block_col = "white", threshold=0.02)
plot_image_explanation(to_df(expl_dog_0170_vgg16), display='block', block_col = "white", threshold=0.015)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.01)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.005)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='outline', outline_col = c("green","red"), threshold=0.005)
plot_image_explanation(to_df(expl_motorbike_0032_resnet50), display='block', block_col = "white", threshold=0.02)
plot_image_explanation(to_df(expl_motorbike_0032_resnet50), display='block', block_col = "white", threshold=0.015)
plot_image_explanation(to_df(expl_motorbike_0032_resnet50), display='block', block_col = "white", threshold=0.01)
plot_image_explanation(to_df(expl_flower_0269_resnet50), display='block', block_col = "white", threshold=0.06)
plot_image_explanation(to_df(expl_flower_0269_resnet50), display='block', block_col = "white", threshold=0.02)
plot_image_explanation(to_df(expl_flower_0269_resnet50), display='block', block_col = "white", threshold=0.015)
plot_image_explanation(to_df(expl_flower_0269_resnet50), display='block', block_col = "white", threshold=0.01)
plot_image_explanation(to_df(expl_flower_0269_resnet50), display='block', block_col = "white", threshold=0.00005)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.04)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.02)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.015)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.01)
plot_image_explanation(to_df(expl_dog_0170_resnet50), display='block', block_col = "white", threshold=0.005)
expls_resnet <- c('flor_resnet_00005.png','flor_resnet_001.png','flor_resnet_0015.png','flor_resnet_002.png','flor_resnet_006.png',
'moto_resnet_001.png','moto_resnet_0015.png','moto_resnet_002.png',
'perro_resnet_004.png','perro_resnet_0005.png','perro_resnet_001.png','perro_resnet_0015.png','perro_resnet_002.png')
for (expl in expls_resnet){
res <- predict(model_resnet50, image_prep(expl))
pred <- imagenet_decode_predictions(res)
print(expl)
print(pred)
}## [1] "flor_resnet_00005.png"
## [[1]]
## class_name class_description score
## 1 n03443371 goblet 0.55889320
## 2 n04522168 vase 0.18716650
## 3 n03888257 parachute 0.04991456
## 4 n11939491 daisy 0.04841070
## 5 n06359193 web_site 0.01254371
##
## [1] "flor_resnet_001.png"
## [[1]]
## class_name class_description score
## 1 n11939491 daisy 0.28510550
## 2 n04522168 vase 0.24932671
## 3 n03443371 goblet 0.19348635
## 4 n03888257 parachute 0.05217402
## 5 n02280649 cabbage_butterfly 0.03128443
##
## [1] "flor_resnet_0015.png"
## [[1]]
## class_name class_description score
## 1 n04522168 vase 0.39725581
## 2 n11939491 daisy 0.19774289
## 3 n03443371 goblet 0.17575277
## 4 n03888257 parachute 0.06895842
## 5 n03991062 pot 0.01821515
##
## [1] "flor_resnet_002.png"
## [[1]]
## class_name class_description score
## 1 n04522168 vase 0.42114443
## 2 n11939491 daisy 0.23756297
## 3 n03888257 parachute 0.04077648
## 4 n02280649 cabbage_butterfly 0.03015097
## 5 n03991062 pot 0.02884009
##
## [1] "flor_resnet_006.png"
## [[1]]
## class_name class_description score
## 1 n04522168 vase 0.30719513
## 2 n03443371 goblet 0.14172523
## 3 n03485794 handkerchief 0.09345738
## 4 n03888257 parachute 0.04718548
## 5 n06359193 web_site 0.03323954
##
## [1] "moto_resnet_001.png"
## [[1]]
## class_name class_description score
## 1 n03792782 mountain_bike 0.10805873
## 2 n03126707 crane 0.09858414
## 3 n02708093 analog_clock 0.09082291
## 4 n03127747 crash_helmet 0.06038012
## 5 n04552348 warplane 0.03419653
##
## [1] "moto_resnet_0015.png"
## [[1]]
## class_name class_description score
## 1 n03127747 crash_helmet 0.07377856
## 2 n03792782 mountain_bike 0.07167485
## 3 n06359193 web_site 0.06592266
## 4 n03888257 parachute 0.05935331
## 5 n03126707 crane 0.05655099
##
## [1] "moto_resnet_002.png"
## [[1]]
## class_name class_description score
## 1 n02708093 analog_clock 0.780441761
## 2 n02794156 barometer 0.066270776
## 3 n04548280 wall_clock 0.040770110
## 4 n03000684 chain_saw 0.012807607
## 5 n03447721 gong 0.008527793
##
## [1] "perro_resnet_004.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.792773306
## 2 n02093256 Staffordshire_bullterrier 0.187786549
## 3 n02108089 boxer 0.013415515
## 4 n02108422 bull_mastiff 0.002793905
## 5 n02087394 Rhodesian_ridgeback 0.001912791
##
## [1] "perro_resnet_0005.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.8295236230
## 2 n02093256 Staffordshire_bullterrier 0.1514441073
## 3 n02108089 boxer 0.0103839682
## 4 n02087394 Rhodesian_ridgeback 0.0043983711
## 5 n02108422 bull_mastiff 0.0006879895
##
## [1] "perro_resnet_001.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.778642774
## 2 n02093256 Staffordshire_bullterrier 0.198025256
## 3 n02087394 Rhodesian_ridgeback 0.011567560
## 4 n02108089 boxer 0.007423394
## 5 n02108422 bull_mastiff 0.002569195
##
## [1] "perro_resnet_0015.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.74800014
## 2 n02087394 Rhodesian_ridgeback 0.09011326
## 3 n02093256 Staffordshire_bullterrier 0.07451496
## 4 n02108089 boxer 0.03279092
## 5 n02109047 Great_Dane 0.01214166
##
## [1] "perro_resnet_002.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.768633604
## 2 n02093256 Staffordshire_bullterrier 0.111421354
## 3 n02087394 Rhodesian_ridgeback 0.054265834
## 4 n02108089 boxer 0.050750721
## 5 n02088466 bloodhound 0.003280226expls_vgg <- c('flor_vgg16_001.png','flor_vgg16_0015.png','flor_vgg16_002.png','flor_vgg16_003.png',
'moto_vgg16_001.png','moto_vgg16_0015.png','moto_vgg16_002.png',
'perro_vgg16_0005.png','perro_vgg16_001.png','perro_vgg16_0015.png','perro_vgg16_002.png','perro_vgg16_003.png')for (expl in expls_vgg){
res <- predict(model_vgg16, image_prep(expl))
pred <- imagenet_decode_predictions(res)
print(expl)
print(pred)
}## [1] "flor_vgg16_001.png"
## [[1]]
## class_name class_description score
## 1 n03944341 pinwheel 0.26917592
## 2 n03991062 pot 0.23156908
## 3 n04522168 vase 0.13489044
## 4 n01833805 hummingbird 0.04371330
## 5 n02843684 birdhouse 0.01517356
##
## [1] "flor_vgg16_0015.png"
## [[1]]
## class_name class_description score
## 1 n03991062 pot 0.25171217
## 2 n03944341 pinwheel 0.21441177
## 3 n04522168 vase 0.15763204
## 4 n01833805 hummingbird 0.02829394
## 5 n02843684 birdhouse 0.01404496
##
## [1] "flor_vgg16_002.png"
## [[1]]
## class_name class_description score
## 1 n03944341 pinwheel 0.40724400
## 2 n03991062 pot 0.16375154
## 3 n04522168 vase 0.07807587
## 4 n01833805 hummingbird 0.02749854
## 5 n02843684 birdhouse 0.02396108
##
## [1] "flor_vgg16_003.png"
## [[1]]
## class_name class_description score
## 1 n03991062 pot 0.23978890
## 2 n04522168 vase 0.08391342
## 3 n12267677 acorn 0.06200504
## 4 n01833805 hummingbird 0.03943241
## 5 n01560419 bulbul 0.02918246
##
## [1] "moto_vgg16_001.png"
## [[1]]
## class_name class_description score
## 1 n03785016 moped 0.13955890
## 2 n03127747 crash_helmet 0.08995814
## 3 n03791053 motor_scooter 0.08621930
## 4 n04509417 unicycle 0.06037048
## 5 n04482393 tricycle 0.04572330
##
## [1] "moto_vgg16_0015.png"
## [[1]]
## class_name class_description score
## 1 n03785016 moped 0.24345194
## 2 n03127747 crash_helmet 0.08737680
## 3 n03792782 mountain_bike 0.07551998
## 4 n03791053 motor_scooter 0.07453740
## 5 n04509417 unicycle 0.04126880
##
## [1] "moto_vgg16_002.png"
## [[1]]
## class_name class_description score
## 1 n03903868 pedestal 0.35849673
## 2 n04509417 unicycle 0.26250902
## 3 n02825657 bell_cote 0.08290558
## 4 n03126707 crane 0.02982016
## 5 n03792782 mountain_bike 0.02433574
##
## [1] "perro_vgg16_0005.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.76940113
## 2 n02093256 Staffordshire_bullterrier 0.09453634
## 3 n02107574 Greater_Swiss_Mountain_dog 0.01457082
## 4 n02108089 boxer 0.01187047
## 5 n02087394 Rhodesian_ridgeback 0.01104719
##
## [1] "perro_vgg16_001.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.71197659
## 2 n02093256 Staffordshire_bullterrier 0.13505511
## 3 n02101388 Brittany_spaniel 0.01619459
## 4 n02091244 Ibizan_hound 0.01220415
## 5 n02108089 boxer 0.01136750
##
## [1] "perro_vgg16_0015.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.859513700
## 2 n02093256 Staffordshire_bullterrier 0.119303264
## 3 n02108089 boxer 0.009755081
## 4 n02087394 Rhodesian_ridgeback 0.003335267
## 5 n02108422 bull_mastiff 0.002183239
##
## [1] "perro_vgg16_002.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.788916826
## 2 n02093256 Staffordshire_bullterrier 0.180674523
## 3 n02108089 boxer 0.013026480
## 4 n02087394 Rhodesian_ridgeback 0.004287839
## 5 n02110806 basenji 0.002374324
##
## [1] "perro_vgg16_003.png"
## [[1]]
## class_name class_description score
## 1 n02093428 American_Staffordshire_terrier 0.785372317
## 2 n02093256 Staffordshire_bullterrier 0.159573287
## 3 n02108089 boxer 0.029370276
## 4 n02108422 bull_mastiff 0.005781381
## 5 n02087394 Rhodesian_ridgeback 0.003781967